Macao air quality forecast using statistical methods

Detalhes bibliográficos
Autor(a) principal: Lei, Man Tat
Data de Publicação: 2019
Outros Autores: Monjardino, Joana, Mendes, Luisa, Ferreira, Francisco
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/126078
Resumo: UID/AMB/04085/2019
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spelling Macao air quality forecast using statistical methodsair pollutantsair quality forecastmanagementmodellingmonitoringSDG 3 - Good Health and Well-beingSDG 11 - Sustainable Cities and CommunitiesUID/AMB/04085/2019The levels of air pollution in the cities of Greater Bay Area in Southern China, including Macao, are extremely high and often exceeded the levels recommended by World Health Organization Air Quality Guidelines. In order for the population to take precautionary measures and avoid further health risks un- der high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression analysis were developed successfully for Macao to predict the next-day concentrations of particulate matter (PM10 and PM2.5) for Taipa Ambient, a background representative station located within the area of Macao (32.9 km2), at Taipa Grande, the headquarter of Macao Meteorological and Geophysical Bureau. The two developed models were statistically significantly valid, with a 95% confidence level with high coefficients of determination. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. The meteorological variables such as geopotential height and relative humidity at different vertical levels were selected from an extensive list of variables. The air quality variables that translate the resilience of the recent past concentrations of each pollutant were the ones selected. The models were based in meteorological and air quality variables with five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes, with high coefficients of determination between predicted and observed daily average concentrations (0.92 and 0.89 for PM10 and PM2.5 , respectively). The results are expected to be the basis for an operational air quality forecast for the region.DCEA - Departamento de Ciências e Engenharia do AmbienteCENSE - Centro de Investigação em Ambiente e SustentabilidadeRUNLei, Man TatMonjardino, JoanaMendes, LuisaFerreira, Francisco2021-10-13T23:20:06Z2019-07-192019-07-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/126078eng2398-2640PURE: 34255519https://doi.org/10.2495/EI-V2-N3-249-258info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:06:46Zoai:run.unl.pt:10362/126078Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:51.163119Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Macao air quality forecast using statistical methods
title Macao air quality forecast using statistical methods
spellingShingle Macao air quality forecast using statistical methods
Lei, Man Tat
air pollutants
air quality forecast
management
modelling
monitoring
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
title_short Macao air quality forecast using statistical methods
title_full Macao air quality forecast using statistical methods
title_fullStr Macao air quality forecast using statistical methods
title_full_unstemmed Macao air quality forecast using statistical methods
title_sort Macao air quality forecast using statistical methods
author Lei, Man Tat
author_facet Lei, Man Tat
Monjardino, Joana
Mendes, Luisa
Ferreira, Francisco
author_role author
author2 Monjardino, Joana
Mendes, Luisa
Ferreira, Francisco
author2_role author
author
author
dc.contributor.none.fl_str_mv DCEA - Departamento de Ciências e Engenharia do Ambiente
CENSE - Centro de Investigação em Ambiente e Sustentabilidade
RUN
dc.contributor.author.fl_str_mv Lei, Man Tat
Monjardino, Joana
Mendes, Luisa
Ferreira, Francisco
dc.subject.por.fl_str_mv air pollutants
air quality forecast
management
modelling
monitoring
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
topic air pollutants
air quality forecast
management
modelling
monitoring
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
description UID/AMB/04085/2019
publishDate 2019
dc.date.none.fl_str_mv 2019-07-19
2019-07-19T00:00:00Z
2021-10-13T23:20:06Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/126078
url http://hdl.handle.net/10362/126078
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2398-2640
PURE: 34255519
https://doi.org/10.2495/EI-V2-N3-249-258
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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